code: def test_generate(art_warning):
      try:
          x_train = np.ones((2, 12, 299, 299, 3)).astype(np.float32)
          y_train = np.zeros((2, 101))
          y_train[:, 1] = 1
  
          model = Model()
          classifier = PyTorchClassifier(
              model=model,
              loss=None,
              input_shape=x_train.shape[1:],
              nb_classes=y_train.shape[1],
              clip_values=(0, 1),
          )
          attack = OverTheAirFlickeringPyTorch(
              classifier=classifier, max_iter=1, verbose=False
          )
  
          x_train_adv = attack.generate(x=x_train, y=y_train)
  
          assert x_train.shape == x_train_adv.shape
          assert np.min(x_train_adv) >= 0.0
          assert np.max(x_train_adv) <= 1.0
  
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/attacks/evasion/test_over_the_air.py
imports: import logging
  import numpy
  import pytest
  import torch
  from art.attacks.evasion.over_the_air_flickering.over_the_air_flickering_pytorch import OverTheAirFlickeringPyTorch
  from art.estimators.classification import PyTorchClassifier
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin
  from art.estimators.classification.classifier import ClassifierMixin
  from tests.attacks.utils import backend_test_classifier_type_check_fail
  from tests.utils import ARTTestException
label_desc: Use image node_id 2 for calling a global function with example usage: test_generate(art_warning) without return types